Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.
KOMPARASI ALGORITMA NAIVE BAYES DAN SUPPORT VECTOR MACHINE UNTUK ANALISA SENTIMEN REVIEW FILM
Film is a subject of interest by a large number of people among the social networking community who have significant differences in their opinions or sentiments. Sentiment analysis or opinion mining is one solution to overcome the problem to classify opinions or reviews into positive or negative opinions automatically. The technique used in this study is Naive Bayes and Support Vector Machines (SVM). Naive Bayes has advantages that are simple, fast and have high accuracy. Whereas SVM is able to identify a separate hyperplane that maximizes the margin between two different classes. The results of the sentiment classification in this study consisted of two class labels, namely positive and negative. The value of accuracy produced will be a benchmark for finding the best testing model for sentiment classification cases. Evaluation is done using 10 fold cross validation. Accuracy measurements were measured by confusion matrix and ROC curve. The results showed that the accuracy value for the Naive Bayes algorithm was 84.50%. While the accuracy value of the Support Vector Machine (SVM) algorithm is greater than Naive Bayes which is equal to 90.00%.
Dhande, L. L., & Patnaik, P. G. K. (2014). Analyzing Sentiment of Movie Review Data using Naive Bayes Neural Classifier. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 3(4), 313–320.
Haddi, E., Liu, X., & Shi, Y. (2013). The Role of Text Pre-processing in Sentiment Analysis. First International Conference on Information Technology and Quantitative Management, 17, 26–32. https://doi.org/10.1016/j.procs.2013.05.05.
Indrayuni, E. (2018). Laporan Akhir Penelitian Mandiri 2018.
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal. https://doi.org/10.1016/j.asej.2014.04.011.
Moraes, R., Valiati, J. F., & Gavião Neto, W. P. (2013). Document-level sentiment classification: An empirical comparison between SVM and ANN. Expert Systems with Applications, 40(2), 621–633. https://doi.org/10.1016/j.eswa.2012.07.05.
Samad, A., Basari, H., Hussin, B., Pramudya, I. G., & Zeniarja, J. (2013). Opinion Mining of Movie Review using Hybrid Method of Support Vector Machine and Particle Swarm Optimization. Procedia Engineering, 53, 453–462. https://doi.org/10.1016/j.proeng.2013.02.059.
Zhang, L., Hua, K., Wang, H., Qian, G., & Zheng, L. (2014). Sentiment analysis on reviews of mobile users. Procedia Computer Science, 34, 458–465. https://doi.org/10.1016/j.procs.2014.07.013
Abstract viewed = 130 times
PDF downloaded = 661 times
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to the PILAR Nusa Mandiri journal as the publisher of the journal, and the author also holds the copyright without restriction.
Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations. The reproduction of any part of this journal, its storage in databases, and its transmission by any form or media, such as electronic, electrostatic and mechanical copies, photocopies, recordings, magnetic media, etc. , are allowed with written permission from the PILAR Nusa Mandiri journal.
PILAR Nusa Mandiri journal, the Editors and the Advisory International Editorial Board make every effort to ensure that no wrong or misleading data, opinions, or statements be published in the journal. In any way, the contents of the articles and advertisements published in the PILAR Nusa Mandiri journal are the sole and exclusive responsibility of their respective authors and advertisers.